Noro's CrossMASimple strategy. Price and moving average crossing. There is a choice of type of moving average.
Moving average types
SMA = Simple Moving Average
EMA = Exponential Moving Average
VWMA = Volume-Weighted Moving Average
DEMA = Double Exponential Moving Average
TEMA = Triple Exponential Moving Average
KAMA = Kaufman's Adaptive Moving Average
PCMA = Central line of price channel (Donchian channel)
Komut dosyalarını "Exponential Moving Average" için ara
Well Rounded Moving AverageIntroduction
There are tons of filters, way to many, and some of them are redundant in the sense they produce the same results as others. The task to find an optimal filter is still a big challenge among technical analysis and engineering, a good filter is the Kalman filter who is one of the more precise filters out there. The optimal filter theorem state that : The optimal estimator has the form of a linear observer , this in short mean that an optimal filter must use measurements of the inputs and outputs, and this is what does the Kalman filter. I have tried myself to Kalman filters with more or less success as well as understanding optimality by studying Linear–quadratic–Gaussian control, i failed to get a complete understanding of those subjects but today i present a moving average filter (WRMA) constructed with all the knowledge i have in control theory and who aim to provide a very well response to market price, this mean low lag for fast decision timing and low overshoots for better precision.
Construction
An good filter must use information about its output, this is what exponential smoothing is about, simple exponential smoothing (EMA) is close to a simple moving average and can be defined as :
output = output(1) + α(input - output(1))
where α (alpha) is a smoothing constant, typically equal to 2/(Period+1) for the EMA.
This approach can be further developed by introducing more smoothing constants and output control (See double/triple exponential smoothing - alpha-beta filter) .
The moving average i propose will use only one smoothing constant, and is described as follow :
a = nz(a ) + alpha*nz(A )
b = nz(b ) + alpha*nz(B )
y = ema(a + b,p1)
A = src - y
B = src - ema(y,p2)
The filter is divided into two components a and b (more terms can add more control/effects if chosen well) , a adjust itself to the output error and is responsive while b is independent of the output and is mainly smoother, adding those components together create an output y , A is the output error and B is the error of an exponential moving average.
Comparison
There are a lot of low-lag filters out there, but the overshoots they induce in order to reduce lag is not a great effect. The first comparison is with a least square moving average, a moving average who fit a line in a price window of period length .
Lsma in blue and WRMA in red with both length = 100 . The lsma is a bit smoother but induce terrible overshoots
ZLMA in blue and WRMA in red with both length = 100 . The lag difference between each moving average is really low while VWRMA is way more precise.
Hull MA in blue and WRMA in red with both length = 100 . The Hull MA have similar overshoots than the LSMA.
Reduced overshoots moving average (ROMA) in blue and WRMA in red with both length = 100 . ROMA is an indicator i have made to reduce the overshoots of a LSMA, but at the end WRMA still reduce way more the overshoots while being smoother and having similar lag.
I have added a smoother version, just activate the extra smooth option in the indicator settings window. Here the result with length = 200 :
This result is a little bit similar to a 2 order Butterworth filter. Our filter have more overshoots which in this case could be useful to reduce the error with edges since other low pass filters tend to smooth their amplitude thus reducing edge estimation precision.
Conclusions
I have presented a well rounded filter in term of smoothness/stability and reactivity. Try to add more terms to have different results, you could maybe end up with interesting results, if its the case share them with the community :)
As for control theory i have seen neural networks integrated to Kalman flters which leaded to great accuracy, AI is everywhere and promise to be a game a changer in real time data smoothing. So i asked myself if it was possible for a neural networks to develop pinescript indicators, if yes then i could be replaced by AI ? Brrr how frightening.
Thanks for reading :)
Moving Average RibbonThis is an extension of the Madrid Moving Average Ribbon public script to allow for different kinds of moving averages (the original allows only exponential and simple). Possible entries in the MA Type argument field are:
sma (simple moving average)
ema (exponential moving average)
wma (weighted moving average)
trima (triangular moving average)
zlema (zero-lag exponential moving average)
dema (double exponential moving average)
tema (triple exponential moving average)
hma (hull moving average)
If the argument given by the user does not match anything from the above list, it will default to ema.
Daily Delta TrendDaily Delta Trend is a useful exponential moving average of the 50 day and 200 day simple moving average. In the first Daily Delta Trend I realized that the simple moving averages were pretty choppy as they were buy then sell over short period of times. So I thought taking an average of another average would smooth my results and give it buy and sell signals more clearly. In chart 1, you can see that it is choppy, and in chart 2 is much smoother.
The way I've been interpreting the chart is to trade it only when the 50-day average (GREEN) Trades with 200-day average (RED). For example, when red and green are both >0 = Buy and both <0 = Sell.
Just from a little of pretesting, I was able to find solid trades from multiple pairs.
DISCLAIMER, I have not actually traded this indicator as I just wrote it for the past few hours, But I thought it was interesting and maybe I might trade it. Feel free to play with it and comment back :P
Volume Momentum [BackQuant]Volume Momentum
The Volume Momentum indicator is designed to help traders identify shifts in market momentum based on volume data. By analyzing the relative volume momentum, this indicator provides insights into whether the market is gaining strength (uptrend) or losing momentum (downtrend). The strategy uses a combination of percentile-based volume normalization, weighted moving averages (WMA), and exponential moving averages (EMA) to assess volume trends.
The system focuses on the relationship between price and volume, utilizing normalized volume data to highlight key market changes. This approach allows traders to focus on volume-driven price movements, helping them to capture momentum shifts early.
Key Features
1. Volume Normalization and Percentile Calculation:
The signed volume (positive when the close is higher than the open, negative when the close is lower) is normalized against the rolling average volume. This normalized volume is then subjected to a percentile interpolation, allowing for a robust statistical measure of how the current volume compares to historical data. The percentile level is customizable, with 50 representing the median.
2. Weighted and Smoothed Moving Averages for Trend Detection:
The normalized volume is smoothed using weighted moving averages (WMA) and exponential moving averages (EMA). These smoothing techniques help eliminate noise, providing a clearer view of the underlying momentum. The WMA filters out short-term fluctuations, while the EMA ensures that the most recent data points have a higher weight, making the system more responsive to current market conditions.
3. Trend Reversal Detection:
The indicator detects momentum shifts by evaluating whether the volume momentum crosses above or below zero. A positive volume momentum indicates a potential uptrend, while a negative momentum suggests a possible downtrend. These trend reversals are identified through crossover and crossunder conditions, triggering alerts when significant changes occur.
4. Dynamic Trend Background and Bar Coloring:
The script offers customizable background coloring based on the trend direction. When volume momentum is positive, the background is colored green, indicating a bullish trend. When volume momentum is negative, the background is colored red, signaling a bearish trend. Additionally, the bars themselves can be colored based on the trend, further helping traders quickly visualize market momentum.
5. Alerts for Momentum Shifts:
The system provides real-time alerts for traders to monitor when volume momentum crosses a critical threshold (zero), signaling a trend reversal. The alerts notify traders when the market momentum turns bullish or bearish, assisting them in making timely decisions.
6. Customizable Parameters for Flexible Usage:
Users can fine-tune the behavior of the indicator by adjusting various parameters:
Volume Rolling Mean: The period used to calculate the average volume for normalization.
Percentile Interpolation Length: Defines the range over which the percentile is calculated.
Percentile Level: Determines the percentile threshold (e.g., 50 for the median).
WMA and Smoothing Periods: Control the smoothing and response time of the indicator.
7. Trend Background Visualization and Trend-Based Bar Coloring:
The background fill is shaded according to whether the volume momentum is positive or negative, providing a visual cue to indicate market strength. Additionally, bars can be color-coded to highlight the trend, making it easier to see the trend’s direction without needing to analyze numerical data manually.
8. Note on Mean-Reversion Strategy:
If you take the inverse of the signals, this indicator can be adapted for a mean-reversion strategy. Instead of following the trend, the strategy would involve buying assets that are underperforming and selling assets that are overperforming, based on volume momentum. However, it’s important to note that this approach may not work effectively on highly correlated assets, as their price movements may be too similar, reducing the effectiveness of the mean-reversion strategy.
Final Thoughts
The Volume Momentum indicator offers a comprehensive approach to analyzing volume-based momentum shifts in the market. By using volume normalization, percentile interpolation, and smoothed moving averages, this system helps identify the strength and direction of market trends. Whether used for trend-following or adapted for mean-reversion, this tool provides traders with actionable insights into the market’s volume-driven movements, improving decision-making and portfolio management.
Quadruple EMA (QEMA)The Quadruple Exponential Moving Average (QEMA) is an advanced technical indicator that extends the concept of lag reduction beyond TEMA (Triple Exponential Moving Average) to a fourth order. By applying a sophisticated four-stage EMA cascade with optimized coefficient distribution, QEMA provides the ultimate evolution in EMA-based lag reduction techniques.
Unlike traditional compund moving averages like DEMA and TEMA, QEMA implements a progressive smoothing system that strategically distributes alphas across four EMA stages and combines them with balanced coefficients (4, -6, 4, -1). This approach creates an indicator that responds extremely quickly to price changes while still maintaining sufficient smoothness to be useful for trading decisions. QEMA is particularly valuable for traders who need the absolute minimum lag possible in trend identification.
▶️ **Core Concepts**
Fourth-order processing: Extends the EMA cascade to four stages for maximum possible lag reduction while maintaining a useful signal
Progressive alpha system: Uses mathematically derived ratio-based alpha progression to balance responsiveness across all four EMA stages
Optimized coefficients: Employs calculated weights (4, -6, 4, -1) to effectively eliminate lag while preserving compound signal stability
Numerical stability control: Implements initialization and alpha distribution to ensure consistent results from the first calculation bar
QEMA achieves its exceptional lag reduction by combining four progressive EMAs with mathematically optimized coefficients. The formula is designed to maximize responsiveness while minimizing the overshoot problems that typically occur with aggressive lag reduction techniques. The implementation uses a ratio-based alpha progression that ensures each EMA stage contributes appropriately to the final result.
▶️ **Common Settings and Parameters**
Period: Default: 15| Base smoothing period | When to Adjust: Decrease for extremely fast signals, increase for more stable output
Alpha: Default: auto | Direct control of base smoothing factor | When to Adjust: Manual setting allows precise tuning beyond standard period settings
Source: Default: Close | Data point used for calculation | When to Adjust: Change to HL2 or HLC3 for more balanced price representation
Pro Tip: Professional traders often use QEMA with longer periods than other moving averages (e.g., QEMA(20) instead of EMA(10)) since its extreme lag reduction provides earlier signals even with longer periods.
▶️ **Calculation and Mathematical Foundation**
Simplified explanation:
QEMA works by calculating four EMAs in sequence, with each EMA taking the previous one as input. It then combines these EMAs using balancing weights (4, -6, 4, -1) to create a moving average with extremely minimal lag and high level of smoothness. The alpha factors for each EMA are progressively adjusted using a mathematical ratio to ensure balanced responsiveness across all stages.
Technical formula:
QEMA = 4 × EMA₁ - 6 × EMA₂ + 4 × EMA₃ - EMA₄
Where:
EMA₁ = EMA(source, α₁)
EMA₂ = EMA(EMA₁, α₂)
EMA₃ = EMA(EMA₂, α₃)
EMA₄ = EMA(EMA₃, α₄)
α₁ = 2/(period + 1) is the base smoothing factor
r = (1/α₁)^(1/3) is the derived ratio
α₂ = α₁ × r, α₃ = α₂ × r, α₄ = α₃ × r are the progressive alphas
Mathematical Rationale for the Alpha Cascade:
The QEMA indicator employs a specific geometric progression for its smoothing factors (alphas) across the four EMA stages. This design is intentional and aims to optimize the filter's performance. The ratio between alphas is **r = (1/α₁)^(1/3)** - derived from the cube root of the reciprocal of the base alpha.
For typical smoothing (α₁ < 1), this results in a sequence of increasing alpha values (α₁ < α₂ < α₃ < α₄), meaning that subsequent EMAs in the cascade are progressively faster (less smoothed). This specific progression, when combined with the QEMA coefficients (4, -6, 4, -1), is chosen for the following reasons:
1. Optimized Frequency Response:
Using the same alpha for all EMA stages (as in a naive multi-EMA approach) can lead to an uneven frequency response, potentially causing over-shooting of certain frequencies or creating undesirable resonance. The geometric progression of alphas in QEMA helps to create a more balanced and controlled filter response across a wider range of movement frequencies. Each stage's contribution to the overall filtering characteristic is more harmonized.
2. Minimized Phase Lag:
A key goal of QEMA is extreme lag reduction. The specific alpha cascade, particularly the relationship defined by **r**, is designed to minimize the cumulative phase lag introduced by the four smoothing stages, while still providing effective noise reduction. Faster subsequent EMAs contribute to this reduced lag.
🔍 Technical Note: The ratio-based alpha progression is crucial for balanced response. The ratio r is calculated as the cube root of 1/α₁, ensuring that the combined effect of all four EMAs creates a mathematically optimal response curve. All EMAs are initialized with the first source value rather than using progressive initialization, eliminating warm-up artifacts and providing consistent results from the first bar.
▶️ **Interpretation Details**
QEMA provides several key insights for traders:
When price crosses above QEMA, it signals the beginning of an uptrend with minimal delay
When price crosses below QEMA, it signals the beginning of a downtrend with minimal delay
The slope of QEMA provides immediate insight into trend direction and momentum
QEMA responds to price reversals significantly faster than other moving averages
Multiple QEMA lines with different periods can identify immediate support/resistance levels
QEMA is particularly valuable in fast-moving markets and for short-term trading strategies where speed of signal generation is critical. It excels at capturing the very beginning of trends and identifying reversals earlier than any other EMA-derived indicator. This makes it especially useful for breakout trading and scalping strategies where getting in early is essential.
▶️ **Limitations and Considerations**
Market conditions: Can generate excessive signals in choppy, sideways markets due to its extreme responsiveness
Overshooting: The aggressive lag reduction can create some overshooting during sharp reversals
Calculation complexity: Requires four separate EMA calculations plus coefficient application, making it computationally more intensive
Parameter sensitivity: Small changes in the base alpha or period can significantly alter behavior
Complementary tools: Should be used with momentum indicators or volatility filters to confirm signals and reduce false positives
▶️ **References**
Mulloy, P. (1994). "Smoothing Data with Less Lag," Technical Analysis of Stocks & Commodities .
Ehlers, J. (2001). Rocket Science for Traders . John Wiley & Sons.
EMA 21 and SMA 50 Low ConditionsDescription:
This indicator highlights trend zones on a daily chart using the 21-day Exponential Moving Average (EMA) and 50-day Simple Moving Average (SMA). It’s designed to identify bullish conditions with two distinct background colors:
• Green Background: Signals a strong bullish trend. Appears when the low of the candle stays above the 21 EMA for 3 or more consecutive days, with either the 3rd or 4th day closing higher than its open (an “up” day). The green zone persists until a candle closes below the 21 EMA.
• Yellow Background: Indicates a potential support zone. Triggers when the low of the candle remains above the 50 SMA after the green condition ends, suggesting the price is still holding above a longer-term average. The yellow zone lasts until a candle closes below the 50 SMA.
Features:
• Plots the 21 EMA (blue line) and 50 SMA (orange line) for visual reference.
• Uses background colors to mark trend zones, making it easy to spot bullish phases and support levels.
• Optimized for daily timeframes, ideal for swing traders or long-term trend followers.
How to Use:
1. Apply the indicator to a daily chart.
2. Watch for the green background to identify strong bullish momentum (lows holding above the 21 EMA with an up close confirmation).
3. Look for the yellow background as a sign of potential support after the short-term trend weakens (lows above the 50 SMA).
4. Exit zones are triggered by closes below the respective averages (21 EMA for green, 50 SMA for yellow).
Notes:
• Best used on symbols with sufficient historical data to ensure accurate EMA and SMA calculations.
• The indicator prioritizes the green condition over yellow—green will override if both could apply.
Author’s Intent:
Created to help traders visualize sustained bullish trends and key support levels using simple moving average rules. Perfect for confirming uptrends and monitoring pullbacks within a broader bullish context.
Volume Flow with Bollinger Bands and EMA Cross SignalsThe Volume Flow with Bollinger Bands and EMA Cross Signals indicator is a custom technical analysis tool designed to identify potential buy and sell signals based on several key components:
Volume Flow: This component combines price movement and trading volume to create a signal that indicates the strength or weakness of price movements. When the price is rising with increasing volume, it suggests strong buying activity, whereas falling prices with increasing volume indicate strong selling pressure.
Bollinger Bands: Bollinger Bands consist of three lines:
The Basis (middle line), which is a Simple Moving Average (SMA) of the price over a set period.
The Upper Band, which is the Basis plus a multiple of the standard deviation (typically 2).
The Lower Band, which is the Basis minus a multiple of the standard deviation. Bollinger Bands help identify periods of high volatility and potential overbought/oversold conditions. When the price touches the upper band, it might indicate that the market is overbought, while touching the lower band might indicate oversold conditions.
EMA Crossovers: The script includes two Exponential Moving Averages (EMAs):
Fast EMA: A shorter-term EMA, typically more sensitive to price changes.
Slow EMA: A longer-term EMA, responding slower to price changes. The crossover of the Fast EMA crossing above the Slow EMA (bullish crossover) signals a potential buy opportunity, while the Fast EMA crossing below the Slow EMA (bearish crossover) signals a potential sell opportunity.
Background Color and Candle Color: The indicator highlights the chart's background with specific colors based on the signals:
Green background for buy signals.
Yellow background for sell signals. Additionally, the candles are colored green for buy signals and yellow for sell signals to visually reinforce the trade opportunities.
Buy/Sell Labels: Small labels are placed on the chart:
"BUY" label in green is placed below the bar when a buy signal is generated.
"SELL" label in yellow is placed above the bar when a sell signal is generated.
Working of the Indicator:
Volume Flow Calculation: The Volume Flow is calculated by multiplying the price change (current close minus the previous close) with the volume. This product is then smoothed with a Simple Moving Average (SMA) over a user-defined period (length). The result is then multiplied by a multiplier to adjust its sensitivity.
Price Change = close - close
Volume Flow = Price Change * Volume
Smoothed Volume Flow = SMA(Volume Flow, length)
The Volume Flow Signal is then: Smooth Volume Flow * Multiplier
This calculation represents the buying or selling pressure in the market.
Bollinger Bands: Bollinger Bands are calculated using the Simple Moving Average (SMA) of the closing price (basis) and the Standard Deviation (stdev) of the price over a period defined by the user (bb_length).
Basis (Middle Band) = SMA(close, bb_length)
Upper Band = Basis + (bb_std_dev * Stdev)
Lower Band = Basis - (bb_std_dev * Stdev)
The upper and lower bands are plotted alongside the price to identify the price's volatility. When the price is near the upper band, it could be overbought, and near the lower band, it could be oversold.
EMA Crossovers: The Fast EMA and Slow EMA are calculated using the Exponential Moving Average (EMA) function. The crossovers are detected by checking:
Buy Signal (Bullish Crossover): When the Fast EMA crosses above the Slow EMA.
Sell Signal (Bearish Crossover): When the Fast EMA crosses below the Slow EMA.
The long_condition variable checks if the Fast EMA crosses above the Slow EMA, and the short_condition checks if it crosses below.
Visual Signals:
Background Color: The background is colored green for a buy signal and yellow for a sell signal. This gives an immediate visual cue to the trader.
Bar Color: The candles are colored green for buy signals and yellow for sell signals.
Labels:
A "BUY" label in green appears below the bar when the Fast EMA crosses above the Slow EMA.
A "SELL" label in yellow appears above the bar when the Fast EMA crosses below the Slow EMA.
Summary of Buy/Sell Logic:
Buy Signal:
The Fast EMA crosses above the Slow EMA (bullish crossover).
Volume flow is positive, indicating buying pressure.
Background turns green and candles are colored green.
A "BUY" label appears below the bar.
Sell Signal:
The Fast EMA crosses below the Slow EMA (bearish crossover).
Volume flow is negative, indicating selling pressure.
Background turns yellow and candles are colored yellow.
A "SELL" label appears above the bar.
Usage of the Indicator:
This indicator is designed to help traders identify potential entry (buy) and exit (sell) points based on:
The interaction of Exponential Moving Averages (EMAs).
The strength and direction of Volume Flow.
Price volatility using Bollinger Bands.
By combining these components, the indicator provides a comprehensive view of market conditions, helping traders make informed decisions on when to enter and exit trades.
NasyI## NasyI - Multi-Timeframe Technical Analysis Toolkit
### English Description
**NasyI** is a comprehensive technical analysis indicator designed to provide traders with a complete view of market dynamics across multiple timeframes. This indicator combines the power of Exponential Moving Averages (EMAs), Simple Moving Averages (MAs), Volume Weighted Average Price (VWAP), and key support/resistance levels to help traders identify trend direction, potential reversal points, and optimal entry/exit opportunities.
#### Key Features
1. **Multi-Timeframe Analysis System**
- 2-minute EMAs (13, 48) for ultra-short-term trend identification
- 5-minute EMAs (9, 13, 21, 48, 200) for short-term trend confirmation
- Daily EMAs (5, 13, 21, 48, 100, 200) and MAs (20, 50, 100, 200) for longer-term perspective
- Color-coded bands between key EMAs to visually identify trend strength and direction
2. **Advanced VWAP Integration**
- Daily VWAP for intraday support/resistance
- Weekly VWAP for medium-term price reference
- Monthly VWAP for long-term institutional price levels
- All VWAPs properly reset at their respective time period boundaries
3. **Critical Price Level Identification**
- Previous day high/low lines for identifying key breakout and breakdown levels
- Pre-market high/low tracking to identify potential intraday support/resistance zones
- All levels displayed with distinct line styles for easy identification
4. **Dynamic Trend Analysis**
- Color-coded bands between EMAs display trend strength and direction:
- Green bands indicate uptrend conditions (9 EMA > 21 EMA > 48 EMA)
- Red bands indicate downtrend conditions (9 EMA < 21 EMA < 48 EMA)
- Yellow bands indicate neutral/confused market conditions
- Visual representation makes trend changes immediately apparent
5. **Comprehensive Customization Options**
- Fully customizable colors for all indicators and bands
- Adjustable transparency settings for visual clarity
- Optional price labels with customizable placement and appearance
- Ability to show/hide specific components based on trading preferences
#### Trading Applications
This indicator is particularly valuable for:
1. **Day Trading & Scalping**: The 2-minute and 5-minute EMAs with color bands provide clear short-term trend direction and potential reversal signals.
2. **Swing Trading**: Daily EMAs and MAs offer perspective on the larger trend, helping to align short-term trades with the broader market direction.
3. **Gap Trading**: Previous day and pre-market levels help identify potential gap fill scenarios and breakout/breakdown opportunities.
4. **VWAP Trading Strategies**: Multiple timeframe VWAPs allow for identifying institutional participation levels and potential reversal zones.
5. **EMA Cross Systems**: The various EMAs can be used to identify golden crosses and death crosses across multiple timeframes.
#### How the Components Work Together
The power of NasyI comes from the integration of these different technical elements:
1. The short-timeframe EMAs (2m, 5m) provide immediate trend information, while the daily EMAs/MAs provide context about the larger market structure.
2. The color bands between EMAs offer instant visual confirmation of trend alignment or divergence across timeframes.
3. Previous day and pre-market levels add horizontal support/resistance zones to complement the dynamic moving averages.
4. Multiple timeframe VWAPs provide additional confirmation of institutional activity levels and potential reversal points.
By combining these elements, traders can develop a comprehensive market view that integrates price action, trend direction, and key support/resistance levels all in one indicator.
#### Usage Instructions
1. Apply the NasyI indicator to your chart (works best on intraday timeframes from 1-minute to 30-minute).
2. Observe the relationship between price and the various EMAs:
- Price above the 2m/5m EMAs with green bands indicates bullish short-term conditions
- Price below the 2m/5m EMAs with red bands indicates bearish short-term conditions
3. Use the daily EMAs/MAs and VWAPs as targets for potential price movements and reversal zones.
4. Previous day and pre-market high/low lines provide key levels to watch for breakouts or breakdowns.
5. Customize the appearance according to your preferences using the extensive settings options.
This indicator represents a unique approach to technical analysis by combining multiple timeframe perspectives into a single, visually intuitive display that helps traders make more informed decisions based on a comprehensive view of market conditions.
### 中文描述
**NasyI** 是一个全面的技术分析指标，旨在为交易者提供跨多个时间周期的完整市场动态视图。该指标结合了指数移动平均线（EMA）、简单移动平均线（MA）、成交量加权平均价格（VWAP）和关键支撑/阻力水平的力量，帮助交易者识别趋势方向、潜在反转点和最佳进出场机会。
#### 主要特点
1. **多时间周期分析系统**
- 2分钟EMAs（13，48）用于超短期趋势识别
- 5分钟EMAs（9，13，21，48，200）用于短期趋势确认
- 日线EMAs（5，13，21，48，100，200）和MAs（20，50，100，200）用于更长期的视角
- 关键EMAs之间的彩色带状区域直观显示趋势强度和方向
2. **高级VWAP整合**
- 日内VWAP作为日内支撑/阻力
- 周内VWAP作为中期价格参考
- 月内VWAP作为长期机构价格水平
- 所有VWAP在各自的时间周期边界正确重置
3. **关键价格水平识别**
- 前一交易日高点/低点线用于识别关键突破和跌破水平
- 盘前高点/低点跟踪用于识别潜在的日内支撑/阻力区域
- 所有水平以不同的线条样式显示，便于识别
4. **动态趋势分析**
- EMAs之间的彩色带状区域显示趋势强度和方向：
- 绿色带状区域表示上升趋势（9 EMA > 21 EMA > 48 EMA）
- 红色带状区域表示下降趋势（9 EMA < 21 EMA < 48 EMA）
- 黄色带状区域表示中性/混乱市场条件
- 视觉表示使趋势变化立即显现
5. **全面的自定义选项**
- 所有指标和带状区域的颜色完全可定制
- 可调节的透明度设置，提高视觉清晰度
- 可选的价格标签，带有可定制的位置和外观
- 能够根据交易偏好显示/隐藏特定组件
#### 交易应用
此指标对以下方面特别有价值：
1. **日内交易和短线交易**：2分钟和5分钟EMAs与色带提供清晰的短期趋势方向和潜在反转信号。
2. **摇摆交易**：日线EMAs和MAs提供对更大趋势的视角，帮助将短期交易与更广泛的市场方向对齐。
3. **缺口交易**：前一日和盘前水平帮助识别潜在的缺口填充情况和突破/跌破机会。
4. **VWAP交易策略**：多时间周期VWAP允许识别机构参与水平和潜在反转区域。
5. **EMA交叉系统**：各种EMAs可用于识别跨多个时间周期的黄金交叉和死亡交叉。
#### 组件如何协同工作
NasyI的强大之处在于这些不同技术元素的集成：
1. 短时间周期EMAs（2m，5m）提供即时趋势信息，而日线EMAs/MAs提供关于更大市场结构的背景。
2. EMAs之间的色带提供趋势对齐或跨时间周期分歧的即时视觉确认。
3. 前一日和盘前水平添加水平支撑/阻力区域，补充动态移动平均线。
4. 多时间周期VWAP提供机构活动水平和潜在反转点的额外确认。
通过结合这些元素，交易者可以发展出全面的市场视图，整合价格行动、趋势方向和关键支撑/阻力水平于一个指标中。
#### 使用说明
1. 将NasyI指标应用到您的图表上（最适合1分钟至30分钟的日内时间周期）。
2. 观察价格与各种EMAs之间的关系：
- 价格位于2m/5m EMAs之上，带有绿色带状区域，表示看涨的短期条件
- 价格位于2m/5m EMAs之下，带有红色带状区域，表示看跌的短期条件
3. 使用日线EMAs/MAs和VWAPs作为潜在价格移动和反转区域的目标。
4. 前一日和盘前高点/低点线提供需要关注的突破或跌破的关键水平。
5. 使用广泛的设置选项根据您的偏好自定义外观。
这个指标代表了一种独特的技术分析方法，将多个时间周期的视角结合到一个单一的、视觉直观的显示中，帮助交易者基于对市场条件的全面视图做出更明智的决策。
[F.B]_ZLEMA MACD ZLEMA MACD – A Zero-Lag Variant of the Classic MACD
Introduction & Motivation
The Moving Average Convergence Divergence (MACD) is a standard indicator for measuring trend strength and momentum. However, it suffers from the latency of traditional Exponential Moving Averages (EMAs).
This variant replaces EMAs with Zero Lag Exponential Moving Averages (ZLEMA), reducing delay and increasing the indicator’s responsiveness. This can potentially lead to earlier trend change detection, especially in highly volatile markets.
Calculation Methodology
2.1 Zero-Lag Exponential Moving Average (ZLEMA)
The classic EMA formula is extended with a correction factor:
ZLEMA_t = EMA(2 * P_t - EMA(P_t, L), L)
where:
P_t is the closing price,
L is the smoothing period length.
2.2 MACD Calculation Using ZLEMA
MACD_t = ZLEMA_short,t - ZLEMA_long,t
with standard parameters of 12 and 26 periods.
2.3 Signal Line with Adaptive Methodology
The signal line can be calculated using ZLEMA, EMA, or SMA:
Signal_t = f(MACD, S)
where f is the chosen smoothing function and S is the period length.
2.4 Histogram as a Measure of Momentum Changes
Histogram_t = MACD_t - Signal_t
An increasing histogram indicates a relative acceleration in trend strength.
Potential Applications in Data Analysis
Since the indicator is based solely on price time series, its effectiveness as a standalone trading signal is limited. However, in quantitative models, it can be used as a feature for trend quantification or for filtering market phases with strong trend dynamics.
Potential use cases include:
Trend Classification: Segmenting market phases into "trend" vs. "mean reversion."
Momentum Regime Identification: Analyzing histogram dynamics to detect increasing or decreasing trend strength.
Signal Smoothing: An alternative to classic EMA smoothing in more complex multi-factor models.
Important: Using this as a standalone trading indicator without additional confirmation mechanisms is not recommended, as it does not demonstrate statistical superiority over other momentum indicators.
Evaluation & Limitations
✅ Advantages:
Reduced lag compared to the classic MACD.
Customizable signal line smoothing for different applications.
Easy integration into existing analytical pipelines.
⚠️ Limitations:
Not a standalone trading system: Like any moving average, this indicator is susceptible to noise and false signals in sideways markets.
Parameter sensitivity: Small changes in period lengths can lead to significant signal deviations, requiring robust optimization.
Conclusion
The ZLEMA MACD is a variant of the classic MACD with reduced latency, making it particularly useful for analytical purposes where faster adaptation to price movements is required.
Its application in trading strategies should be limited to multi-factor models with rigorous evaluation. Backtests and out-of-sample analyses are essential to avoid overfitting to past market data.
Disclaimer: This indicator is provided for informational and educational purposes only and does not constitute financial advice. The author assumes no responsibility for any trading decisions made based on this indicator. Trading involves significant risk, and past performance is not indicative of future results.
Mile Runner - Swing Trade LONGMile Runner - Swing Trade LONG Indicator - By @jerolourenco
Overview
The Mile Runner - Swing Trade LONG indicator is designed for swing traders who focus on LONG positions in stocks, BDRs (Brazilian Depositary Receipts), and ETFs. It provides clear entry signals, stop loss, and take profit levels, helping traders identify optimal buying opportunities with a robust set of technical filters. The indicator is optimized for daily candlestick charts and combines multiple technical analysis tools to ensure high-probability trades.
Key Features
Entry Signals: Visualized as green triangles below the price bars, indicating a potential LONG entry.
Stop Loss and Take Profit Levels: Automatically plotted on the chart for easy reference.
Stop Loss: Based on the most recent pivot low (support level).
Take Profit: Calculated using a Fibonacci-based projection from the entry price to the stop loss.
Trend and Momentum Filters: Ensures trades align with the prevailing trend and have sufficient momentum.
Volume and Volatility Confirmation: Verifies market interest and price movement potential.
How It Works
The indicator uses a combination of technical tools to filter and confirm trade setups:
Exponential Moving Averages (EMAs):
A short EMA (default: 9 periods) and a long EMA (default: 21 periods) identify the trend.
A bullish crossover (EMA9 crosses above EMA21) signals a potential upward trend.
Money Flow Index (MFI):
Confirms buying pressure when MFI > 50.
Average True Range (ATR):
Ensures sufficient volatility by checking if ATR exceeds its 20-period moving average.
Volume:
Confirms market interest when volume exceeds its 20-period moving average.
Pivot Lows:
Identifies recent support levels (pivot lows) to set the stop loss.
Ensures the pivot low is recent (within the last 10 bars by default).
Additional Trend Filter:
Confirms the long EMA is rising, reinforcing the bullish trend.
Inputs and Customization
The indicator is highly customizable, allowing traders to tailor it to their strategies:
EMA Periods: Adjust the short and long EMA lengths.
ATR and MFI Periods: Modify lookback periods for volatility and momentum.
Pivot Lookback: Control the sensitivity of pivot low detection.
Fibonacci Level: Adjust the Fibonacci retracement level for take profit.
Take Profit Multiplier: Fine-tune the aggressiveness of the take profit target.
Max Pivot Age: Set the maximum bars since the last pivot low for relevance.
Usage Instructions
Apply the Indicator:
Add the "Mile Runner - Swing Trade LONG" indicator to your TradingView chart.
Best used on daily charts for swing trading.
Look for Entry Signals:
A green triangle below the price bar signals a potential LONG entry.
Set Stop Loss and Take Profit:
Stop Loss: Red dashed line indicating the stop loss level.
Take Profit: Purple dashed line showing the take profit level.
Monitor the Trade:
The entry price is marked with a green dashed line for reference.
Adjust trade management based on the plotted levels.
Set Alerts:
Use the built-in alert condition to get notified of new LONG entry signals.
Important Notes
For LONG Positions Only : Designed exclusively for swing trading LONG positions.
Timeframe: Optimized for daily charts but can be tested on other timeframes.
Asset Types: Works best with stocks, BDRs, and ETFs.
Risk Management: Always align stop loss and take profit levels with your risk tolerance.
Why Use Mile Runner?
The Mile Runner indicator simplifies swing trading by integrating trend, momentum, volume, and volatility filters into one user-friendly tool. It helps traders:
Identify high-probability entry points.
Establish clear stop loss and take profit levels.
Avoid low-volatility or low-volume markets.
Focus on assets with strong buying pressure and recent support.
By following its signals and levels, traders can make informed decisions and enhance their swing trading performance. Customize the inputs and test it on your favorite assets—happy trading!
Cometreon_Public📚 Cometreon Public Library – Advanced Functions for Pine Script
This library contains advanced functions used in my public indicators on TradingView. The goal is to make the code more modular and efficient, allowing users to call pre-built functions for complex calculations without rewriting them from scratch.
🔹 Currently Available Functions:
1️⃣ Moving Average Function – Provides multiple types of moving averages to choose from, including:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
RMA (Smoothed Moving Average)
HMA (Hull Moving Average)
JMA (Jurik Moving Average)
DEMA (Double Exponential Moving Average)
TEMA (Triple Exponential Moving Average)
LSMA (Least Squares Moving Average)
VWMA (Volume-Weighted Moving Average)
SMMA (Smoothed Moving Average)
KAMA (Kaufman’s Adaptive Moving Average)
ALMA (Arnaud Legoux Moving Average)
FRAMA (Fractal Adaptive Moving Average)
VIDYA (Variable Index Dynamic Average)
2️⃣ Custom RSI – Uses the Moving Average function to modify the calculation method, with an additional option for a dynamic version.
3️⃣ Custom MACD – Uses the Moving Average function to modify the calculation method, with an additional option for a dynamic version.
4️⃣ Custom Alligator – Uses the Moving Average function to modify generic calculations, allowing users to change the calculation method.
Consecutive Bars Above/Below EMA Buy the Dip Strategy█ STRATEGY DESCRIPTION
The "Consecutive Bars Above/Below EMA Buy the Dip Strategy" is a mean-reversion strategy designed to identify potential buying opportunities when the price dips below a moving average for a specified number of consecutive bars. It enters a long position when the dip condition is met and exits when the price shows strength by exceeding the previous bar's high. This strategy is suitable for use on various timeframes.
█ WHAT IS THE MOVING AVERAGE?
The strategy uses either a Simple Moving Average (SMA) or an Exponential Moving Average (EMA) as a reference for identifying dips. The type and length of the moving average can be customized in the settings.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The close price is below the selected moving average for a specified number of consecutive bars (`consecutiveBarsTreshold`).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the current closing price exceeds the high of the previous bar (`close > high `). This indicates that the price has shown strength, potentially confirming the reversal and prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
Consecutive Bars Threshold: The number of consecutive bars the price must remain below the moving average to trigger a Buy Signal. Default is 3.
MA Type: The type of moving average used (SMA or EMA). Default is SMA.
MA Length: The length of the moving average. Default is 5.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for mean-reverting markets and performs best when the price frequently oscillates around the moving average.
It is sensitive to the number of consecutive bars below the moving average, which helps to identify potential dips.
Backtesting results should be analysed to optimize the Consecutive Bars Threshold, MA Type, and MA Length for specific instruments.
[blackcat] L2 Enhanced MACD Trend█ OVERVIEW
The Enhanced MACD Trend script combines traditional Moving Average Convergence Divergence (MACD) analysis with On-Balance Volume (OBV) insights to provide traders with a comprehensive understanding of market trends. By examining both price momentum and volume fluctuations, this tool aids in identifying potential upward or downward market transitions.
█ LOGICAL FRAMEWORK
Initially, the script prompts users to configure fundamental parameters such as the speed of moving averages. It subsequently utilizes a specialized auxiliary function named calculate_macd_obv_signals to perform intricate computations. This function calculates the discrepancy between two distinct types of moving averages (captured via MACD analysis), evaluates the direction of capital inflows and outflows within securities (using OBV), and applies smoothing techniques to mitigate undue influence from minor fluctuations. Ultimately, visual representations of these calculations are rendered on an additional chart pane for enhanced interpretability.
█ CUSTOM FUNCTIONS
Function: calculate_macd_obv_signals
• Purpose: Determines critical aspects associated with MACD and OBV.
• Parameters:
• fastLength (int): Dictates the responsiveness of the shorter Exponential Moving Average (EMA) to price variations.
• slowLength (int): Specifies the reactivity of the longer EMA.
• signalSmoothing (int): Defines the degree of smoothness applied to the divergence between EMAs.
• Functionality:
• macd_diff: Illustrates whether price increases have accelerated relative to previous levels or decelerated, providing insight into existing momentum.
• macd_signal_line: Smoothens macd_diff values, serving akin to a trailing indicator for macd_diff.
• macd_histogram: Visually accentuates disparities between macd_diff and macd_signal_line employing color-coded bars, facilitating identification of significant divergences.
• obv_signal: Represents a refined variant of short-term OBV concentrating solely on periods characterized by elevated buying interest, aiding in reduction of extraneous signals.
• moving_average_short: Analyzes recent closing prices across several sessions to corroborate burgeoning bullish or bearish tendencies.
• Returns: An array encompassing .
█ KEY POINTS AND TECHNIQUES
Advanced Features: Employs sophisticated functions including ta.ema() and ta.sma(), enabling accurate calculation of EMAs and SMAs respectively, thus enhancing precision in trend detection.
Optimization Techniques: Incorporates customizable inputs (input.int) permitting strategic adjustments alongside scrutiny of escalating or declining volumes to accurately gauge genuine sentiment shifts while discounting insignificant anomalies.
Best Practices: Maintains separation between algorithmic processes and graphical outputs, preserving organizational clarity; hence simplifying debugging efforts and future enhancements.
Unique Approaches: Integrates multifaceted assessments simultaneously – amalgamating candlestick formations and volumetric activities – offering a holistic perspective instead of reliance on singular indicators. Consequently, delivers astute recommendations grounded in diverse analytical underpinnings rather than speculative forecasts.
█ EXTENDED KNOWLEDGE AND APPLICATIONS
Potential Modifications:
1 — Implement automated alert mechanisms signaling crossover events pinpointing optimal buy/sell junctures to fine-tune timing preemptively minimizing losses proactively.
2 — Enable user customization of sensitivity criteria governing trigger intensity thereby eliminating trivial aberrations and emphasizing substantial patterns exclusively.
Application Scenarios:
Beneficial for high-frequency trading aiming to capitalize on fleeting price movements swiftly. Suitable for dynamic environments necessitating rapid responses due to frequent market volatility demanding prompt reactions. Perfect for individuals engaging in regular transactions seeking unparalleled accuracy navigating fluctuating circumstances ensuring consistent profitability amidst disturbances maintaining steady yields irrespective of upheavals.
Related Concepts:
Contemplate interactions among oscillators (such as MACD) and volume metrics detecting instances wherein they oppose each other (indicative of divergences) or concur (signaling crossovers). Profound comprehension of these interrelationships substantially refines trading strategies integrating broader economic factors, seasonal influences guiding overarching plans resulting in heightened predictive capabilities elevating trading effectiveness leveraging cumulative information transforming unprocessed statistics into actionable intelligence empowering informed decisions advancing confidently toward objectives effortlessly scaling achievements seamlessly realizing aspirations effortlessly.
Median Deviation Suite [InvestorUnknown]The Median Deviation Suite uses a median-based baseline derived from a Double Exponential Moving Average (DEMA) and layers multiple deviation measures around it. By comparing price to these deviation-based ranges, it attempts to identify trends and potential turning points in the market. The indicator also incorporates several deviation types—Average Absolute Deviation (AAD), Median Absolute Deviation (MAD), Standard Deviation (STDEV), and Average True Range (ATR)—allowing traders to visualize different forms of volatility and dispersion. Users should calibrate the settings to suit their specific trading approach, as the default values are not optimized.
Core Components
Median of a DEMA:
The foundation of the indicator is a Median applied to the 7-day DEMA (Double Exponential Moving Average). DEMA aims to reduce lag compared to simple or exponential moving averages. By then taking a median over median_len periods of the DEMA values, the indicator creates a robust and stable central tendency line.
float dema = ta.dema(src, 7)
float median = ta.median(dema, median_len)
Multiple Deviation Measures:
Around this median, the indicator calculates several measures of dispersion:
ATR (Average True Range): A popular volatility measure.
STDEV (Standard Deviation): Measures the spread of price data from its mean.
MAD (Median Absolute Deviation): A robust measure of variability less influenced by outliers.
AAD (Average Absolute Deviation): Similar to MAD, but uses the mean absolute deviation instead of median.
Average of Deviations (avg_dev): The average of the above four measures (ATR, STDEV, MAD, AAD), providing a combined sense of volatility.
Each measure is multiplied by a user-defined multiplier (dev_mul) to scale the width of the bands.
aad = f_aad(src, dev_len, median) * dev_mul
mad = f_mad(src, dev_len, median) * dev_mul
stdev = ta.stdev(src, dev_len) * dev_mul
atr = ta.atr(dev_len) * dev_mul
avg_dev = math.avg(aad, mad, stdev, atr)
Deviation-Based Bands:
The indicator creates multiple upper and lower lines based on each deviation type. For example, using MAD:
float mad_p = median + mad // already multiplied by dev_mul
float mad_m = median - mad
Similar calculations are done for AAD, STDEV, ATR, and the average of these deviations. The indicator then determines the overall upper and lower boundaries by combining these lines:
float upper = f_max4(aad_p, mad_p, stdev_p, atr_p)
float lower = f_min4(aad_m, mad_m, stdev_m, atr_m)
float upper2 = f_min4(aad_p, mad_p, stdev_p, atr_p)
float lower2 = f_max4(aad_m, mad_m, stdev_m, atr_m)
This creates a layered structure of volatility envelopes. Traders can observe which layers price interacts with to gauge trend strength.
Determining Trend
The indicator generates trend signals by assessing where price stands relative to these deviation-based lines. It assigns a trend score by summing individual signals from each deviation measure. For instance, if price crosses above the MAD-based upper line, it contributes a bullish point; crossing below an ATR-based lower line contributes a bearish point.
When the aggregated trend score crosses above zero, it suggests a shift towards a bullish environment; crossing below zero indicates a bearish bias.
// Define Trend scores
var int aad_t = 0
if ta.crossover(src, aad_p)
aad_t := 1
if ta.crossunder(src, aad_m)
aad_t := -1
var int mad_t = 0
if ta.crossover(src, mad_p)
mad_t := 1
if ta.crossunder(src, mad_m)
mad_t := -1
var int stdev_t = 0
if ta.crossover(src, stdev_p)
stdev_t := 1
if ta.crossunder(src, stdev_m)
stdev_t := -1
var int atr_t = 0
if ta.crossover(src, atr_p)
atr_t := 1
if ta.crossunder(src, atr_m)
atr_t := -1
var int adev_t = 0
if ta.crossover(src, adev_p)
adev_t := 1
if ta.crossunder(src, adev_m)
adev_t := -1
int upper_t = src > upper ? 3 : 0
int lower_t = src < lower ? 0 : -3
int upper2_t = src > upper2 ? 1 : 0
int lower2_t = src < lower2 ? 0 : -1
float trend = aad_t + mad_t + stdev_t + atr_t + adev_t + upper_t + lower_t + upper2_t + lower2_t
var float sig = 0
if ta.crossover(trend, 0)
sig := 1
else if ta.crossunder(trend, 0)
sig := -1
Practical Usage and Calibration
Default settings are not optimized: The given parameters serve as a starting point for demonstration. Users should adjust:
median_len: Affects how smooth and lagging the median of the DEMA is.
dev_len and dev_mul: Influence the sensitivity of the deviation measures. Larger multipliers widen the bands, potentially reducing false signals but introducing more lag. Smaller multipliers tighten the bands, producing quicker signals but potentially more whipsaws.
This flexibility allows the trader to tailor the indicator for various markets (stocks, forex, crypto) and time frames.
Backtesting and Performance Metrics
The code integrates with a backtesting library that allows traders to:
Evaluate the strategy historically
Compare the indicator’s signals with a simple buy-and-hold approach
Generate performance metrics (e.g., mean returns, Sharpe Ratio, Sortino Ratio) to assess historical effectiveness.
Disclaimer
No guaranteed results: Historical performance does not guarantee future outcomes. Market conditions can vary widely.
User responsibility: Traders should combine this indicator with other forms of analysis, appropriate risk management, and careful calibration of parameters.
TechniTrend: Trend and Volume Indicator🟪 Overview
The "TechniTrend: Trend and Volume Indicator" is designed to assist traders in identifying optimal entry and exit points in the market by combining trend detection and volume analysis. This indicator integrates moving average-based trend recognition with volume threshold analysis to provide clear buy and sell signals. Additionally, it includes divergence filtering and correlation analysis to enhance the accuracy of signals, making it suitable for traders who want to identify strong trends and high-probability trading opportunities.
🟪 Indicator Components
🔹Trend Moving Average (MA):
The indicator calculates a moving average based on the closing prices to identify the overall trend. Various moving average types are available for customization:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Hull Moving Average (HMA)
The length of the moving average can be adjusted via the Trend MA Length input to adapt to different market conditions.
🔹Volume Analysis:
A volume moving average is calculated over a specified period (Volume MA Length), with a volume threshold derived by multiplying this average by a user-defined Volume Threshold Factor.
This threshold helps to classify volume as either high or low, which plays a crucial role in signal generation.
🔹Divergence Detection:
The indicator can filter out signals when there is a divergence between the price movement and volume changes. Divergence occurs when the price moves in one direction while the volume moves in the opposite direction, indicating a potential weakening of the trend.
🔹Correlation Analysis:
The correlation between price and volume changes is calculated over the same length as the trend moving average. The user can enable this filter to ensure that buy or sell signals are only generated when the correlation exceeds a specified threshold (Correlation Threshold).
🟪 Signals and Alerts
🔹Buy Signal: Generated when the price is above the trend moving average (indicating an uptrend), and the current volume exceeds the volume threshold (indicating high volume). The signal can be filtered based on divergence or correlation settings.
🔹Sell Signal: Triggered when the price is below the trend moving average (indicating a downtrend), and the volume is above the threshold. Similarly, the signal is affected by divergence and correlation filters.
🔹Visual Indicators:
The trend line is plotted on the chart for easy trend identification.
Buy and sell signals are displayed using green and red labels, respectively.
Background highlighting is used to mark areas of high volume on the chart.
🟪 Customizable Inputs
🔹Trend MA Length: Adjusts the moving average length used to detect trends.
🔹Volume MA Length: Sets the length for the volume moving average calculation.
🔹MA Type for Trend: Select the type of moving average for trend detection (SMA, EMA, WMA, HMA).
🔹Volume Threshold Factor: Multiplier used to define high-volume conditions based on the volume moving average.
🔹Enable Divergence Filter: Allows the user to filter out signals where divergence between price and volume is detected.
🔹Enable Correlation Filter: Enables filtering of signals based on the correlation between price and volume changes.
🔹Correlation Threshold: Sets the minimum correlation value required for a signal to be considered valid.
🟪 Alerts
The indicator provides alert conditions for buy and sell signals, allowing traders to receive notifications when new trading opportunities arise.
🟪 Usage Tips
🔹Adjust Moving Average Lengths: Shorter lengths can make the indicator more sensitive to recent market changes, while longer lengths provide a smoother, more reliable trend signal.
🔹Volume Threshold Factor: Increasing this factor raises the volume required to generate high-volume signals, which can help filter.
🟪 Disclaimer:
The "TechniTrend: Trend and Volume Indicator" is a technical analysis tool intended for educational and informational purposes only. It is not a guarantee of future performance or a recommendation to buy, sell, or hold any financial instrument. Trading involves significant risk, and past performance is not indicative of future results. Users should conduct their own research and seek advice from a qualified financial professional before making any investment decisions. The creators of this indicator are not liable for any financial losses or damages incurred through its use.
Trend Following Moron TFM 10% System
Trend Following Moron TFM 10% System
The TFM 10% Market Timing System
The Trend Following Moron TFM 10% System is a powerful trading tool designed using Pine Script™, following the principles outlined by Dave S. Landry. This script helps traders identify optimal entry and exit points based on moving averages and market trends.
What the Script Does:
Visual representation of trend strength.
As long as it is trending in green band, trend is very strong and price is contained within 5% of the high.
As price drops to yellow band, strength is weakening and caution is advised. Price is between 5% to 10% away from52 week high.
As price drops in red band, it is to be avoided as trend is rolling over. Price is more than 10% way from 52 week high.
Moving Averages Calculation:
Users can choose between Simple Moving Average (SMA) and Exponential Moving Average (EMA) for daily, weekly, and monthly periods. The script calculates the moving averages to provide trend direction.
Trend Color Coding:
Moving averages are displayed in different colors based on market conditions: green indicates an uptrend, red for a downtrend, and gray for neutral conditions.
Highs Calculation:
The script calculates the 52-week and 12-month closing highs, which are crucial for identifying potential breakout points.
Level Definition:
Traders can set levels based on either Average True Range (ATR) or percentage changes from these highs, allowing for flexible risk management strategies.
Buy and Sell Conditions:
The script defines specific buy conditions: when the price is within 10% of the highest close and trading above the moving averages, and sell conditions: when the price falls below these thresholds.
Visual Indicators:
Buy and sell signals are visually represented on the chart with arrows, making it easy for traders to see potential trading opportunities at a glance.
Performance Labels:
The script includes performance labels that track the number of bars above or below the moving averages and the percentage change from the moving average, providing users with key metrics to evaluate their trades.
Interactive Table:
A table summarizing the buy and sell rules is displayed on the chart, ensuring that traders have quick access to the system’s trading logic.
Benefits of Using the TFM 10% System:
Streamlined Decision Making:
The script simplifies the trading process by clearly outlining buy and sell signals, making it accessible even for novice traders.
Customizable Parameters:
Users can tailor the script to their preferences by adjusting moving average types and lengths, ATR levels, and percentage thresholds. Bands are interchange able for ATR and Percent below 52 week high for volatility looks. But buy and sell are fixed in 10% threshold.
Risk Management:
By utilizing ATR and percentage levels, traders can effectively manage their risk, making the trading process more systematic.
Comprehensive Market Analysis:
The combination of multiple time frames (daily, weekly, monthly) allows for a well-rounded analysis of market trends, enhancing trading accuracy.
Post-Open Long Strategy with ATR-based Stop Loss and Take ProfitThe "Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit" is designed to identify buying opportunities after the German and US markets open. It combines various technical indicators to filter entry signals, focusing on breakout moments following price lateralization periods.
Key Components and Their Interaction:
Bollinger Bands (BB):
Description: Uses BB with a 14-period length and standard deviation multiplier of 1.5, creating narrower bands for lower timeframes.
Role in the Strategy: Identifies low volatility phases (lateralization). The lateralization condition is met when the price is near the simple moving average of the BB, suggesting an imminent increase in volatility.
Exponential Moving Averages (EMA):
10-period EMA: Quickly detects short-term trend direction.
200-period EMA: Filters long-term trends, ensuring entries occur in a bullish market.
Interaction: Positions are entered only if the price is above both EMAs, indicating a consolidated positive trend.
Relative Strength Index (RSI):
Description: 7-period RSI with a threshold above 30.
Role in the Strategy: Confirms the market is not oversold, supporting the validity of the buy signal.
Average Directional Index (ADX):
Description: 7-period ADX with 7-period smoothing and a threshold above 10.
Role in the Strategy: Assesses trend strength. An ADX above 10 indicates sufficient momentum to justify entry.
Average True Range (ATR) for Dynamic Stop Loss and Take Profit:
Description: 14-period ATR with multipliers of 2.0 for Stop Loss and 4.0 for Take Profit.
Role in the Strategy: Adjusts exit levels based on current volatility, enhancing risk management.
Resistance Identification and Breakout:
Description: Analyzes the highs of the last 20 candles to identify resistance levels with at least two touches.
Role in the Strategy: A breakout above this level signals a potential continuation of the bullish trend.
Time Filters and Market Conditions:
Trading Hours: Operates only during the opening of the German market (8:00 - 12:00) and US market (15:30 - 19:00).
Panic Candle: The current candle must close negative, leveraging potential emotional reactions in the market.
Avoiding Entry During Pullbacks:
Description: Checks that the two previous candles are not both bearish.
Role in the Strategy: Avoids entering during a potential pullback, improving trade success probability.
Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit
The "Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit" is designed to identify buying opportunities after the German and US markets open. It combines various technical indicators to filter entry signals, focusing on breakout moments following price lateralization periods.
Key Components and Their Interaction:
Bollinger Bands (BB):
Description: Uses BB with a 14-period length and standard deviation multiplier of 1.5, creating narrower bands for lower timeframes.
Role in the Strategy: Identifies low volatility phases (lateralization). The lateralization condition is met when the price is near the simple moving average of the BB, suggesting an imminent increase in volatility.
Exponential Moving Averages (EMA):
10-period EMA: Quickly detects short-term trend direction.
200-period EMA: Filters long-term trends, ensuring entries occur in a bullish market.
Interaction: Positions are entered only if the price is above both EMAs, indicating a consolidated positive trend.
Relative Strength Index (RSI):
Description: 7-period RSI with a threshold above 30.
Role in the Strategy: Confirms the market is not oversold, supporting the validity of the buy signal.
Average Directional Index (ADX):
Description: 7-period ADX with 7-period smoothing and a threshold above 10.
Role in the Strategy: Assesses trend strength. An ADX above 10 indicates sufficient momentum to justify entry.
Average True Range (ATR) for Dynamic Stop Loss and Take Profit:
Description: 14-period ATR with multipliers of 2.0 for Stop Loss and 4.0 for Take Profit.
Role in the Strategy: Adjusts exit levels based on current volatility, enhancing risk management.
Resistance Identification and Breakout:
Description: Analyzes the highs of the last 20 candles to identify resistance levels with at least two touches.
Role in the Strategy: A breakout above this level signals a potential continuation of the bullish trend.
Time Filters and Market Conditions:
Trading Hours: Operates only during the opening of the German market (8:00 - 12:00) and US market (15:30 - 19:00).
Panic Candle: The current candle must close negative, leveraging potential emotional reactions in the market.
Avoiding Entry During Pullbacks:
Description: Checks that the two previous candles are not both bearish.
Role in the Strategy: Avoids entering during a potential pullback, improving trade success probability.
Entry and Exit Conditions:
Long Entry:
The price breaks above the identified resistance.
The market is in a lateralization phase with low volatility.
The price is above the 10 and 200-period EMAs.
RSI is above 30, and ADX is above 10.
No short-term downtrend is detected.
The last two candles are not both bearish.
The current candle is a "panic candle" (negative close).
Order Execution: The order is executed at the close of the candle that meets all conditions.
Exit from Position:
Dynamic Stop Loss: Set at 2 times the ATR below the entry price.
Dynamic Take Profit: Set at 4 times the ATR above the entry price.
The position is automatically closed upon reaching the Stop Loss or Take Profit.
How to Use the Strategy:
Application on Volatile Instruments:
Ideal for financial instruments that show significant volatility during the target market opening hours, such as indices or major forex pairs.
Recommended Timeframes:
Intraday timeframes, such as 5 or 15 minutes, to capture significant post-open moves.
Parameter Customization:
The default parameters are optimized but can be adjusted based on individual preferences and the instrument analyzed.
Backtesting and Optimization:
Backtesting is recommended to evaluate performance and make adjustments if necessary.
Risk Management:
Ensure position sizing respects risk management rules, avoiding risking more than 1-2% of capital per trade.
Originality and Benefits of the Strategy:
Unique Combination of Indicators: Integrates various technical metrics to filter signals, reducing false positives.
Volatility Adaptability: The use of ATR for Stop Loss and Take Profit allows the strategy to adapt to real-time market conditions.
Focus on Post-Lateralization Breakout: Aims to capitalize on significant moves following consolidation periods, often associated with strong directional trends.
Important Notes:
Commissions and Slippage: Include commissions and slippage in settings for more realistic simulations.
Capital Size: Use a realistic trading capital for the average user.
Number of Trades: Ensure backtesting covers a sufficient number of trades to validate the strategy (ideally more than 100 trades).
Warning: Past results do not guarantee future performance. The strategy should be used as part of a comprehensive trading approach.
With this strategy, traders can identify and exploit specific market opportunities supported by a robust set of technical indicators and filters, potentially enhancing their trading decisions during key times of the day.
Inverted SD Dema RSI | viResearchInverted SD Dema RSI | viResearch
The "Inverted SD Dema RSI" developed by viResearch introduces a new approach to trend analysis by combining the Double Exponential Moving Average (DEMA), Standard Deviation (SD), and Relative Strength Index (RSI). This unique indicator provides traders with a tool to capture market trends by integrating volatility-based thresholds. By using the smoothed DEMA along with standard deviation, the indicator offers improved responsiveness to price fluctuations, while RSI thresholds offer insight into overbought and oversold market conditions.
At the core of the "Inverted SD Dema RSI" is the combination of DEMA and standard deviation for a more nuanced view of market volatility. The use of RSI further aids in detecting price extremes and potential trend reversals.
DEMA Calculation (sublen): The Double Exponential Moving Average (DEMA) smoothes out price data over a user-defined period, reducing lag compared to traditional moving averages. This provides a clearer representation of the market's overall direction.
Standard Deviation Calculation (sublen_2): The standard deviation of the DEMA is used to define the upper (u) and lower (d) bands, highlighting areas where price volatility may signal a change in trend. These dynamic bands help traders gauge price volatility and potential breakouts or breakdowns.
RSI Calculation (len): The script applies the Relative Strength Index (RSI) to the smoothed DEMA values, allowing traders to detect momentum shifts based on a modified data set. This provides a more accurate reflection of market strength when combined with the DEMA.
Thresholds: The RSI is compared to user-defined thresholds (70 for overbought and 55 for oversold conditions). These thresholds help in identifying potential market reversals, especially when the price breaks outside of the calculated standard deviation bands.
Uptrend (L): An uptrend signal is generated when the RSI exceeds the upper threshold (70) and the price is not above the upper standard deviation band, indicating that there may be room for further price appreciation.
Downtrend (S): A downtrend signal occurs when the RSI falls below the lower threshold (55), indicating that the price may continue to decline.
The "Inverted SD Dema RSI" offers a wide range of customizable settings, allowing traders to adjust the indicator based on their trading style or market conditions.
DEMA Length (sublen): Controls the period used to smooth the price data, impacting the sensitivity of the DEMA to recent price movements.
Standard Deviation Length (sublen_2): Defines the length over which the standard deviation is calculated, helping traders control the width of the upper and lower bands.
RSI Length (len): Adjusts the period used for the RSI calculation, providing flexibility in determining overbought and oversold conditions.
RSI Thresholds: Traders can define their own levels for detecting trend reversals, with default values of 70 for an uptrend and 55 for a downtrend.
The "Inverted SD Dema RSI" is particularly well-suited for traders looking to capture trends while accounting for volatility and momentum. By using a smoothed DEMA as the foundation, it effectively filters out noise, making it ideal for detecting reliable trends in volatile markets.
Key Uses:
Trend Following: The indicator’s combination of DEMA, standard deviation, and RSI helps traders follow trends more effectively by reducing noise and identifying key momentum shifts.
Volatility Filtering: The use of standard deviation bands provides a dynamic measure of volatility, ensuring that traders are aware of potential breakouts or breakdowns in the market.
Momentum Detection: The inclusion of RSI ensures that the indicator is not only focused on trend direction but also on the strength of the underlying momentum, helping traders avoid entering trades during weak trends.
The "Inverted SD Dema RSI" provides several key advantages over traditional trend-following indicators:
Reduced Lag: The use of DEMA ensures faster trend detection, reducing the lag associated with simple moving averages.
Noise Reduction: The integration of standard deviation helps filter out irrelevant price movements, making it easier to identify significant trends.
Momentum Awareness: The addition of RSI provides valuable insight into the strength of trends, helping traders avoid false signals during periods of weak momentum.
The "Inverted SD Dema RSI" offers a powerful blend of trend-following and momentum detection, making it a versatile tool for modern traders. By integrating DEMA, standard deviation, and RSI, the indicator provides a comprehensive view of market trends and volatility. Traders are encouraged to experiment with different settings for the DEMA length, standard deviation, and RSI thresholds to fine-tune the indicator for their specific trading strategies. Whether used for trend confirmation, volatility assessment, or momentum analysis, the "Inverted SD Dema RSI" offers a valuable tool for traders seeking a comprehensive approach to market analysis.
Brooks Always In [KintsugiTrading]Brooks Always In
Overview:
The "Brooks Always In Indicator" by KintsugiTrading is a tool designed for traders who follow price action methodologies inspired by Al Brooks. This indicator identifies key bar patterns and breakouts, plots an Exponential Moving Average (EMA), and highlights consecutive bullish and bearish bars. It is intended to assist traders in making informed decisions based on price action dynamics.
Features:
Consecutive Bar Patterns:
Identifies and highlights consecutive bullish and bearish bars.
Differentiates between bars that are above/below the EMA and those that are not.
Customizable EMA:
Option to display an Exponential Moving Average (EMA) with user-defined length and offset.
The EMA can be smoothed using various methods such as SMA, EMA, SMMA (RMA), WMA, and VWMA.
Breakout Patterns:
Recognizes bullish and bearish breakout bars and outside bars.
Tracks inside bars and prior bar conditions to better understand the market context.
Customizable Display:
Users can display or hide the EMA, consecutive bar patterns, and consecutive bars relative to the moving average.
How to Use:
Customize Settings:
First, I like to navigate to the top right corner of the chart (bolt icon), and change both the bull and bear body color to match the background (white/black) - this helps the user visualize the indicator far better.
Next, Toggle to display EMA, consecutive bar patterns, and consecutive bars relative to the moving average using the provided input options.
Adjust the EMA length, source, and offset as per your trading strategy.
Select the smoothing method and length for the EMA if desired.
Analyze Key Patterns:
Observe the highlighted bars on the chart to identify consecutive bullish and bearish patterns.
Use the plotted EMA to gauge the general trend and analyze the relationship between price bars and the moving average.
Informed Decision Making:
Utilize the identified bar patterns and breakouts to make informed trading decisions, such as identifying potential entry and exit points based on price action dynamics.
Good luck with your trading!
EMA 9/13/18/25 + Bollinger BandThe indicator combines two components: Exponential Moving Averages (EMAs) and Bollinger Bands.
Exponential Moving Averages (EMAs): The indicator calculates four EMAs with different periods: 9, 13, 18, and 25. An Exponential Moving Average is a type of moving average that places a greater weight and significance on the most recent data points. As the name suggests, it's an average of the asset's price over a certain period, with recent prices given more weight in the calculation, making it more responsive to recent price changes.
Bollinger Bands: Bollinger Bands consist of a simple moving average (the basis) and two standard deviations plotted away from it. The standard deviations are multiplied by a factor (usually 2) to determine the distance from the basis. These bands dynamically adjust themselves based on recent price movements. The upper band represents the highest price level reached in the given period, while the lower band represents the lowest price level.
Combining these components provides traders with insights into both trend direction and volatility. The EMAs help identify trends by smoothing out price data, while the Bollinger Bands offer insights into volatility and potential price reversal points. Traders often use the crossovers of EMAs and interactions with Bollinger Bands to make trading decisions. For example, when the price touches the upper Bollinger Band, it may indicate overbought conditions, while touching the lower band may suggest oversold conditions. Additionally, crossovers of EMAs (such as the shorter-term EMA crossing above or below the longer-term EMA) may signal changes in trend direction.
MTF BB+KC Avg
Bollinger Bands (BB) are a widely used technical analysis created by John Bollinger in the early 1980’s. Bollinger Bands consist of a band of three lines which are plotted in relation to instrument prices. The line in the middle is usually a Simple Moving Average (SMA) set to a period of 20 days (The type of trend line and period can be changed by the trader; however a 20 day moving average is by far the most popular). This indicator does not plot the middle line. The Upper and Lower Bands are used as a way to measure volatility by observing the relationship between the Bands and price. Typically the Upper and Lower Bands are set to two standard deviations away from the middle line, however the number of standard deviations can also be adjusted in the indicator.
Keltner Channels (KC) are banded lines similar to Bollinger Bands and Moving Average Envelopes. They consist of an Upper Envelope above a Middle Line (not plotted in this indicator) as well as a Lower Envelope below the Middle Line. The Middle Line is a moving average of price over a user-defined time period. Either a simple moving average or an exponential moving average are typically used. The Upper and Lower Envelopes are set a (user-defined multiple) of a range away from the Middle Line. This can be a multiple of the daily high/low range, or more commonly a multiple of the Average True Range.
This indicator is built on AVERAGING the BB and KC values for each bar, so you have an efficient metric of AVERAGE volatility. The indicator visualizes changes in volatility which is of course dynamic.
What to look for
High/Low Prices
One thing that must be understood about this indicator's plots is that it averages by adding BB levels to KC levels and dividing by 2. So the plots provide a relative definition of high and low from two very popular indicators. Prices are almost always within the upper and lower bands. Therefore, when prices move up near the upper or lower bands or even break through the band, many traders would see that price action as OVER-EXTENDED (either overbought or oversold, as applicable). This would preset a possible selling or buying opportunity.
Cycling Between Expansion and Contraction
Volatility can generally be seen as a cycle. Typically periods of time with low volatility and steady or sideways prices (known as contraction) are followed by period of expansion. Expansion is a period of time characterized by high volatility and moving prices. Periods of expansion are then generally followed by periods of contraction. It is a cycle in which traders can be better prepared to navigate by using Bollinger Bands because of the indicators ability to monitor ever changing volatility.
Walking the Bands
Of course, just like with any indicator, there are exceptions to every rule and plenty of examples where what is expected to happen, does not happen. Previously, it was mentioned that price breaking above the Upper Band or breaking below the Lower band could signify a selling or buying opportunity respectively. However this is not always the case. “Walking the Bands” can occur in either a strong uptrend or a strong downtrend.
During a strong uptrend, there may be repeated instances of price touching or breaking through the Upper Band. Each time that this occurs, it is not a sell signal, it is a result of the overall strength of the move. Likewise during a strong downtrend there may be repeated instances of price touching or breaking through the Lower Band. Each time that this occurs, it is not a buy signal, it is a result of the overall strength of the move.
Keep in mind that instances of “Walking the Bands” will only occur in strong, defined uptrends or downtrends.
Inputs
TimeFrame
You can select any timeframe froom 1 minute to 12 months for the bar measured.
Length of the internal moving averages
You can select the period of time to be used in calculating the moving averages which create the base for the Upper and Lower Bands. 20 days is the default.
Basis MA Type
Determines the type of Moving Average that is applied to the basis plot line. Default is SMA and you can select EMA.
Source
Determines what data from each bar will be used in calculations. Close is the default.
StdDev/Multiplier
The number of Standard Deviations (for BB) or Multiplier (for KC) away from the moving averages that the Upper and Lower Bands should be. 2 is the default value for each indicator.
Kendall's Tau Correlation Regimes [NariCapitalTrading]The "Kendall's Tau Correlation Regimes" indicator is designed to analyze price data and determine market regimes based on Kendall's Tau correlation coefficient. It provides insight into the strength and direction of the correlation between two data series: close price and a selected moving average.
User Inputs:
Period: Defines the lookback period for calculating Kendall's Tau correlation. It can be adjusted using the input slider, with a minimum value of 1.
Threshold: Sets the threshold for identifying bullish and bearish market regimes. The user can adjust this value within the range of 0.1 to 1.0 with step increments of 0.1.
MA Type: Allows users to select the type of moving average to be used in the correlation calculation. Options include Simple Moving Average (SMA), Exponential Moving Average (EMA), and Hull Moving Average (HMA).
Kendall's Tau Correlation Calculation:
Calculates Kendall's Tau correlation coefficient between the closing price and the selected moving average.
Kendall's Tau measures the strength and direction of the ordinal association between two data series. It assesses whether the data pairs are in the same order or not.
The calculation involves counting concordant and discordant pairs of data points and then computing the coefficient.
Market Regime Identification:
Based on the threshold defined by the user, the indicator identifies two market regimes: bullish and bearish.
A regime is considered bullish when the Kendall's Tau correlation coefficient is greater than the threshold.
A regime is considered bearish when the Kendall's Tau correlation coefficient is less than the negative of the threshold.
Plotting:
The indicator plots the calculated Kendall's Tau correlation coefficient as a blue line on a separate indicator pane.
It also highlights bullish regimes with a green background and bearish regimes with a red background.
Conclusion:
The "Kendall's Tau Correlation Regimes" indicator provides traders with a visual aid for assessing market regimes based on the strength of correlation between price and a selected moving average.
Disclaimer: This indicator is for educational and informational purposes only.
